Kevin kelly new rules for the new economy 10 radical strategies for a connected world

New rules for the new economy : 10 radical strategies for a connected world / Kevin Kelly.. These can be thought of as “enabling sectors.” Computer chips and communication networks have

New Rules for the New Economy

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Copyright © Kevin Kelly, 1998

All rights reserved

A portion of this work fi rst appeared in Wired, September 1997,

as “New Rules for the New Economy: Twelve Dependable Principles for Thriving in a Turbulent World.”

Library of Congress Cataloging-in-Publication Data

Kelly, Kevin.

New rules for the new economy : 10 radical strategies for

a connected world / Kevin Kelly.

This book is printed on acid-free paper.

Printed in the United States of America

Set in Electra

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For Gia-Miin

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This New Economy 1

1 Embrace the Swarm 9

2 Increasing Returns 23

3 Plentitude, Not Scarcity 39

4 Follow the Free 50

5 Feed the Web First 65

6 Let Go at the Top 83

7 From Places to Spaces 94

8 No Harmony, All Flux 108

9 Relationship Tech 118

10 Opportunities Before Effi ciencies 140

A Thousand Points of Wealth 156 New Rules for the New Economy 161

This New Economy

No one can escape the transforming fi re of machines Technology, which once progressed at the periphery of culture, now engulfs our minds as well as our lives Is it any wonder that technology triggers such intense fascination, fear, and rage?

One by one, each of the things that we care about in life is touched

by science and then altered Human expression, thought, tion, and even human life have been infi ltrated by high technology As each realm is overtaken by complex techniques, the usual order is in-verted, and new rules established The mighty tumble, the once confi -dent are left desperate for guidance, and the nimble are given a chance

communica-to prevail

But while the fast-forward technological revolution gets all the lines these days, something much larger is slowly turning beneath it Steadily driving the gyrating cycles of cool technogadgets and gotta-haves is an emerging new economic order The geography of wealth is being reshaped by our tools We now live in a new economy created by shrinking computers and expanding communications

head-This new economy represents a tectonic upheaval in our wealth, a far more turbulent reordering than mere digital hardware has produced The new economic order has its own distinct opportunities and pitfalls If past economic transformations are any guide, those who play by the new rules will prosper, while those who ignore them will not We have seen only the beginnings of the anxiety, loss, excitement, and gains that many people will experience as our world shifts to a new highly technical planetary economy

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This new economy has three distinguishing characteristics: It is global It favors intangible things—ideas, information, and relation-ships And it is intensely interlinked These three attributes produce a new type of marketplace and society, one that is rooted in ubiquitous electronic networks.

Networks have existed in every economy What’s different now is that networks, enhanced and multiplied by technology, penetrate our lives so deeply that “network” has become the central metaphor around which our thinking and our economy are organized Unless we can un-derstand the distinctive logic of networks, we can’t profi t from the eco-nomic transformation now under way

New Rules for the New Economy lays out ten essential dynamics of

this emerging fi nancial order These rules are fundamental principles that are hardwired into this new territory, and that apply to all busi-nesses and industries, not just high-tech ones Think of the principles outlined in this book as rules of thumb

Like any rules of thumb they aren’t infallible Instead, they act as beacons charting out general directions They are designed to illumi-nate deep-rooted forces that will persist into the fi rst half of the next century These ten laws attempt to capture the underlying principles that shape our new economic environment, rather than chase current short-term business trends

The key premise of this book is that the principles governing the world of the soft—the world of intangibles, of media, of software, and

of services—will soon command the world of the hard—the world of reality, of atoms, of objects, of steel and oil, and the hard work done

by the sweat of brows Iron and lumber will obey the laws of software, automobiles will follow the rules of networks, smokestacks will comply with the decrees of knowledge If you want to envision where the future

of your industry will be, imagine it as a business built entirely around the soft, even if at this point you see it based in the hard

Of course, all the mouse clicks in the world can’t move atoms in real space without tapping real energy, so there are limits to how far the soft will infi ltrate the hard But the evidence everywhere indicates that the hard world is irreversibly softening Therefore one can gain a huge advantage simply by riding this conversion To stay ahead, you chiefl y need to understand how the soft world works—how networks pros-

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per and grow, how interfaces control attention, how plentitude drives value—and then apply those principles to the hard world of now.

The tricks of the intangible trade will become the tricks of your trade.

The new economy deals in wispy entities such as information, tionships, copyright, entertainment, securities, and derivatives The U.S economy is already demassifying, drifting toward these intangibles The creations most in demand from the United States (those exported) lost 50% of their physical weight per dollar of value in only six years The disembodied world of computers, entertainment, and telecommuni-cations is now an industry larger than any of the old giants of yore, such as construction, food products, or automobile manufacturing This new information-based sector already occupies 15% of the total U.S economy

rela-Yet digital bits, stock options, copyright, and brands have no surable economic shape What is the unit of software: Floppy disks? Lines of code? Number of programs? Number of features? Economists are baffl ed Walter Wriston, former chairman of Citicorp, likes to grum-ble that federal economists can tell us exactly how many left-handed cowboys are employed each year, yet have no idea how many software programs are in use The dials on our economic dashboard have started spinning wildly, blinking and twittering as we head into new territory It’s possible the gauges are all broken, but it is much more likely the world is turning upside down

mea-Remember GM? In the 1950s business reporters were infatuated with General Motors GM was the paragon of industrial progress It not only made cars, it made America GM was the richest company on earth To many intelligent observers, GM was the future of business in general It was huge, and bigger was better It was stable and paternal, providing lifetime employment It controlled all parts of its vast empire, ensur-ing quality and high profi ts GM was the best, and when the pundits looked ahead 40 years they imagined all successful companies would be like GM

How ironic that ever since the future has arrived, GM is now the counter example Today, if your company is like GM, it’s in deep trou-

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ble Instead, pundits point to Microsoft Microsoft is the role model

It is the highest-valued company on Earth It produces intangibles It rides the logic of standards Its sky-high stock valuation refl ects the new produc tivity So we look ahead and say: In 40 years all companies will

be like Microsoft

History would suggest this is a bad bet The obvious lesson is that we tend to project the future from what’s fashionable at present Right now software and entertainment companies are very profi table, so we assume they are role models Brad DeLong, an economist at UC Berkeley, has

a handy theory of economic history He says that various sectors of economy wax and wane in prominence like movie stars The history

of the American economy can be seen as a parade of “heroic” tries that fi rst appear on the scene as unknowns, then heroically “save” the economy by doing economic miracles, and for a time are treated

indus-as economic stars In the 1900s, the automobile industry windus-as heroic: There was incredible innovation, many, many car company upstarts, in-credible productivity It was a wild and exciting time But then the hero-ism died away and the auto industry became big, monolithic, boring, and hugely profi table In DeLong’s view, the latest heroic savior is the information, communication, and entertainment complex Businesses

in the realm of software and communications are now valorous: They pull successes out of a hat, stack up unending innovation, and perform economic miracles Long live computers!

There is a lot of common sense to DeLong’s view of heroic industry Just because Microsoft is heroic now, doesn’t mean all companies will follow their lead and replicate intellectual property on fl oppy disks with

a profi t margin of 90% No doubt many, many companies in the future will not resemble Microsoft at all Somebody has to fi x the plugged toi-lets of the world, somebody has to build houses, somebody has to drive the trucks hauling our milk

Even Wired magazine, mouthpiece of the digital revolution—where I

serve as one of the editors—does not approach the ideal of an

intangi-ble company Wired is located smack in the middle of an old-fashioned

downtown city, and in one year turns 8 million pounds (or 48 railway cars) of dried tree pulp, and 330,000 pounds of bright colored ink into hard copies of the magazine A lot of atoms are involved

So how can we make the claim that all businesses in the world

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will be reshaped by advances in chips and glass fi bers and spectrum? What makes this particular technological advance so special? Why is the business hero of this moment so much more important than its recent predecessors?

Because communication—which in the end is what the digital nology and media are all about—is not just a sector of the economy

tech-Communication is the economy.

This vanguard is not about computers Computers are over Most of the consequences that we can expect from computers as stand-alone machines have already happened They have sped up our lives, and made managing words, numbers, and pixels quite extraordinary, but they have not had much more effect beyond that

The new economy is about communication, deep and wide All the

transformations suggested in this book stem from the fundamental way

we are revolutionizing communications Communication is the tion of society, of our culture, of our humanity, of our own individual identity, and of all economic systems This is why networks are such a big deal Communication is so close to culture and society itself that the effects of technologizing it are beyond the scale of a mere indus-trial-sector cycle Communication, and its ally computers, is a special case in economic history Not because it happens to be the fashionable leading business sector of our day, but because its cultural, technologi-cal, and conceptual impacts reverberate at the root of our lives

founda-Certain technologies (such as the integrated circuit chip) spur novation and novelty in other technologies; these catalysts are called

in-“enabling technologies.” Occasionally an economic sector will age power and accelerate the advance of other sectors in an economy These can be thought of as “enabling sectors.” Computer chips and communication networks have produced a sector of an economy that is transforming all the other sectors

lever-Only a relatively small number of people have ever been directly employed in the world of fi nance Yet ever since the days of the Vene-tian bankers, fi nancial innovations such as mortgages, insurance, ven-ture funding, stocks, checks, credit cards, mutual funds, to name only

a few, have completely reshaped our economy They have enabled the

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rise of corporations, of market capitalism, of the industrial age, and much more Unlike many previous heroic industries such as the electri-cal power industry or the chemical industry, this small sector has infl u-enced how all business is done, and how we structure our lives.

As tremendous as the infl uence of fi nancial inventions have been, the infl uence of network inventions will be as great, or greater.

It took several billion years on Earth for unicellular life to evolve And it took another billion years or so for that single-celled life to evolve multi cellular arrangements—each cell touching a few cells near it to make a living spherical organism At fi rst, the sphere was the only form multicellular life could take because its cells had to be near one an-other to coordinate their functions After another billion years, life even-tually evolved the fi rst cellular neuron—a thin strand of tissue—which enabled two cells to communicate over a distance With that single enabling innovation, the variety of life boomed With neurons, life no longer had to remain bounded in a blob It was possible to arrange cells into almost any shape, size, and function Butterfl ies, orchids, and kan-garoos all became possible Life quickly exploded in a million different unexpected ways, into fantastic awesome varieties, until wonderful life was everywhere

Silicon chips linked into high-bandwidth channels are the neurons

of our culture Until this moment, our economy has been in the multi cellular stage Our industrial age has required each customer or company to almost physically touch one another Our fi rms and or-ganizations resemble blobs Now, by the enabling invention of silicon and glass neurons, a million new forms are possible Boom! An infi nite variety of new shapes and sizes of social organizations are suddenly possible Unimaginable forms of commerce can now coalesce in this new eco nomy We are about to witness an explosion of entities built on relationships and technology that will rival the early days of life on Earth

in their variety

In the future very few companies will look like Microsoft, or even

Wired Even ancient forms will be bent Farming, and trucking,

plumb-ing, and other traditional occupations will continue, just as unicellular life continues But the economics of farmers and friends, in their own

way, will obey the logic of networks, just as Microsoft does now.

We see evidence for that already A farmer in America—the hero of the agricultural economy—rides in a portable offi ce on his tractor It’s air conditioned, has a phone, a satellite-driven GPS location device, and sophisticated sensors near the ground At home his computer is connected to the never-ending stream of weather data, the worldwide grain markets, his bank, moisture detectors in the soil, digitized maps, and his own spreadsheets of cash fl ow Yes, he gets dirt under his fi n-gernails, but his manual labor takes place in the context of a network economy

Much the same can be said about truck drivers While the ence of sitting behind a wheel remains unchanged, the new tools of trucking—bar codes, radios, dispatch algorithms, route hubs, and even roads themselves—all follow the logic of networks Thus, the very sweat

experi-of truckers as they manually load and unload heavy boxes becomes corporated into the network economy

in-Our economy is an amalgamation of diverse styles of trade, merce, and social exchanges New economic functions develop around the operating old Barter, one of the earliest forms of commerce, has not gone away The barter economy ran through the agricultural age, the industrial age, and continues today Indeed most of what happens on the World Wide Web is barter Even many years from now a signifi cant portion of what the economy does will be done by the industrial lay-ers—machines churning out goods and moving materials The old economies will continue to operate profi tably within the deep cortex of the new economy

com-Yet the inertia of the industrial age continues to mesmerize us tween 1990 and 1996 the number of people making tangible things—stuff you can drop on your toe—decreased by 1%, while the number

Be-of people employed in providing “services” (intangibles) grew 15% Presently a mere 18% of U.S employment is in manufacturing But three quarters of those 18% actually perform network economy jobs while working for a manufacturing company Instead of pushing atoms they push bits around: accountants, researchers, designers, market-ing, sales, lawyers, and all the rest who sit at a desk Only a minus-cule percentage of the workforce performs industrial age tasks, yet our politics, our media, our funding, and our education continue the grand

fantasy that industrial jobs need to be created Within a generation, two at the most, the number of people working in honest-to-goodness manufacturing jobs will be no more than the number of farmers in the land—less than a few percent Far more than we realize it, the network economy is pulling in everyone.

As the world of chips and glass fi bers and wireless waves goes, so goes the rest of the world.

In the face of history this bold assertion may seem naive But every once in a while something big and new does happen It must have felt that way to the home-craft Luddites who sensed that the industrial age was not just about newfangled looms, but foreshadowed deep, systemic changes with life-changing ramifi cations Were they naive to think that machines would ultimately transform the ancient and holy act of plant-ing seeds and harvesting the grain? Of breeding cows? Of the structure

of communities?

“Listen to the technology,” advises Carver Mead, one of the tors of the modern computer chip “Find out what it is telling you.” Following that lead, I have assembled these rules of thumb by asking these questions: How do our tools shape our destiny? What kind of an economy is our new technology suggesting?

inven-Steel ingots and rivers of oil, smokestacks and factory lines, and even tiny seeds and cud-chewing cows are all becoming enmeshed in the world of smart chips and fast bandwidth, and sooner or later they will begin to fully obey the new rules of the new economy, as everything will I’ve listened to the technology, and as best as I can determine, the technology repeats ten distinct refrains, as premiered in the following ten chapters

1 EMBRACE THE SWARM

The Power of Decentralization

The atom is the icon of the 20th century The atom whirls alone It is the metaphor for individuality But the atom is the past The symbol for the next century is the net The net has no center, no orbits, no cer-tainty It is an indefi nite web of causes The net is the archetype dis-played to represent all circuits, all intelligence, all interdependence, all things economic, social, or ecological, all communications, all democ-racy, all families, all large systems, almost all that we fi nd interesting and important Whereas the atom represents clean simplicity, the net channels messy complexity

The net is our future.

Of all the endeavors we humans are now engaged in, perhaps the grandest of them all is the steady weaving together of our lives, minds, and artifacts into a global scale network This great work has been go-ing on for decades, but recently our ability to connect has accelerated Two brand-new technological achievements—the silicon chip and the silicate glass fi ber—have rammed together with incredible speed Like nuclear particles crashing together in a cyclotron, the intersection of these two innovations has unleashed a never-before-seen force: the power of a pervasive net As this grand net spreads, an animated swarm

is reticulating the surface of the planet We are clothing the globe with

a network society

The dynamic of our society, and particularly our new economy,

will increasingly obey the logic of networks Understanding how networks work will be the key to understanding how the economy works.

Any network has two ingredients: nodes and connections In the grand network we are now assembling, the size of the nodes is col-lapsing while the quantity and quality of the connections are exploding These two physical realms, the collapsing microcosm of silicon and the exploding telecosm of connections, form the matrix through which the new economy of ideas fl ows

A single silicon transistor today can only be seen in a microscope

In a few years it will take a microscope to see an entire chip of tors As the size of silicon chips shrinks to the microscopic, their costs shrink to the microscopic as well In 1950 a transistor cost fi ve dollars Today it costs one hundredth of a cent In 2003 one transistor will cost

transis-a microscopic ntransis-anocent A chip with transis-a billion trtransis-ansistors will eventutransis-ally cost only a few cents

What this means is that chips are becoming cheap and tiny enough

to slip into every object we make Eventually, every can of soup will have

a chip on its lid Every light switch will contain a chip Every book will have a chip embedded in its spine Every shirt will have at least one chip sewn into its hem Every item on a grocery shelf will have stuck to it, or embedded within itself, a button of silicon There are 10 trillion objects manufactured in the world each year and the day will come when each one of them will carry a fl ake of silicon

This is not crazy, nor distant Ten years ago the notion that all doors

in a building should contain a computer chip seemed ludicrous, but now there is hardly a hotel door in the U.S without a blinking, beeping chip in its lock These microscopic chips will be so cheap we’ll throw them away Thin slices of plastic known as smart cards now hold a throwaway chip smart enough to be your banker If National Semicon-ductor gets its way, soon every FedEx package will be stamped with a disposable silicon fl ake that smartly tracks the contents of the package

on its journey And if an ephemeral envelope can have a chip, so can your chair, each bag of candy, a new coat, a basketball Soon, all manu-factured objects, from sneakers to drill presses to lamp shades to cans

of soda, will contain a tiny sliver of embedded thought

And why not?

Today the world is populated by 200 million computers Andy Grove

of Intel happily estimates that we’ll see 500 million computers by 2002 Yet for every expensive chip put into a beige computer box, there are now 30 other cheap processors put into everyday things The number

of noncomputer chips already pulsating in the world is 6 billion—one chip for every human on Earth

We are moving from crunching

to connecting While the number of computer chips is rising, the number of chips in objects other than computers is rising faster.

You already have a non-PC chip embedded in your car and stereo and rice cooker and phone These chips are dumb chips, with limited ambitions A chip in your car’s brakes doesn’t have to do fl oating-point math, spreadsheets, or video processing; it only needs to brake like a bulldog

Because they have limited functions and can be produced in great quantity, these dumb chips are ultracheap to make One industry ob-server calculated that an embedded processor chip costs less to manu-facture than a ball bearing Since they can be stamped out as fast and cheap as candy gumdrops, these chips are known in the trade as “jelly beans.” Dumb, cheap jelly bean chips are invading the world far faster than PCs did

This is not surprising You can only use one or two personal puters at a time, but the number of other objects in your life is almost unlimited First, we’ll put jelly bean chips into high-tech appliances, then later into all tools, and then eventually into all objects If current rates continue there’ll be some 10 billion tiny grains of silicon chips embedded into our environment by 2005

com-Putting a dot of intelligence into every object we make at fi rst gives

us a billion dimwitted artifacts But we are also, at the same time, connecting these billion nodes, one by one.

We are connecting everything to everything.

There is something mysterious that happens when we take large numbers of things that are fairly limited and connect them all together When we take the dumb chip in each cash register in a store and link them into a swarm, we have something more than dumb We have real-time buying patterns that can manage inventory If we take the dumb chips that already regulate the guts of an automobile engine, and let them communicate an engine’s performance to the mechanic of a trucking fi rm, those dumb chips can smartly cut expensive road repairs (Mercedes Benz recently announced it is planning to embed a web server into its top-of-the-line model cars so technicians can spot service problems remotely.) When connected into a swarm, small thoughts be-come smart

When we permit any object to transmit a small amount of data and

to receive input from its neighborhood, we change an inert object into

an animated node.

It is not necessary that each connected object transmit much data

A tiny chip plastered inside a water tank on an Australian ranch mits only the telegraphic 2-bit message of whether the tank is FULL or NOT A chip attached to the ear of each steer on the same ranch beams out his location in GPS numbers; nothing more “I’m here, I’m here” it tells the rancher’s log book; nothing more The chip in the gate at the end of the rancher’s road communicates only a single word, reporting when it was last opened: “Tuesday.”

trans-It does not take sophisticated infrastructure to transmit these dumb bits Stationary objects—parts of a building, tools on the factory fl oor,

fi xed cameras—are wired together The nonstationary rest—that is, most manufactured objects—are linked by infrared and radio, creating

a wireless web vastly larger than the wired web The same everyday quencies that run garage door openers and TV remote controls will be multiplied by the millions to carry the dumb messages of connected

The glory of these connected crumbs is that they don’t need to be individually sophisticated They don’t need speech recognition, artifi cial intelligence, or fancy expert systems Instead, the network economy re-lies on the dumb power of bits linked together into a swarm

Our brains tap into dumb power by clumping dumb neurons into consciousness The internet banks on dumb power by connecting dumb personal computers A personal computer is like a single brain neuron in a plastic box When linked by the telecosm into a neural net-work, these dumb PC nodes create that fabulous intelligence called the World Wide Web

Again and again we see the same dynamic at work in other domains: Dumb cells in our body work together in a swarm to produce an incred-ibly smart immune system, a system so sophisticated we still do not fully comprehend it

Dumb parts, properly connected into a swarm, yield smart results.

A trillion dumb chips connected into a hive mind is the hardware The software that runs through it is the network economy A planet covered with hyperlinked chips is shrouded with waves of sensibility Millions of moisture sensors in the fi elds of farmers shoot up data, hundreds of weather satellites beam down digitized images, thousands

of cash registers spit out bit streams, myriad hospital bedside monitors trickle out signals, millions of web sites tally attention, and tens of mil-lions of vehicles transmit their location code; all of this swirls into the web That matrix of signals is the net

The net is not just humans typing at one another on AOL, although that is a part of it and will be as long as seduction and fl aming are enjoyable Rather, the net is the total collective interaction of a trillion objects and living beings, linked together through air and glass

This is the net that begets the network economy According to MCI, data traffi c on the global phone system will soon overtake voice traf-

fi c The current total volume of voice traffi c is 1,000 times that of data, but in three years that ratio will fl ip ElectronicCast estimates data traf-

fi c—the talk of machines—will be ten times voice traffi c by 2005 That means that by 2001 most of the signals zipping around the Earth will

be machines talking to machines—fi le transfers, data streams, and the like The network economy is already expanding to include new partici-pants: agents, bots, objects, and servers, as well as several billion more humans We won’t wait for AI to make intelligent systems; we’ll do it with the swarm power of ubiquitous computing and pervasive connec-tions.

The surest way to smartness is through massive dumbness.

The surest way to advance massive connectionism is to exploit centralized forces—to link the distributed bottom How do you build a better bridge? Let the parts talk to one another How do you improve lettuce farming? Let the soil speak to the farmer’s tractors How do you make aircraft safe? Let the airplanes communicate among themselves and pick their own fl ight paths This decentralized approach, known as

de-“free fl ight,” is a system the FAA is now trying to institute to increase safety and reduce air-traffi c bottlenecks at airports

Mathematical problems which were once intractable for computers have been solved by using a swarm of small PCs A very complex problem is broken up into tiny parts and distributed through-out the network Likewise, vast research projects that would tax any one institution can be distributed to an ad hoc network The Tree of Life is a worldwide taxonomic catalog of all living species on Earth administered

super-on the web Such a project is beysuper-ond the capabilities of super-one perssuper-on or group But a decentralized network can produce the necessary intelli-gence Each local expert supplies their own data (on fi nches, or ferns or jellyfi sh) to fi ll in some of the blanks As Larry Keely of the Doblin Group says, “No one is as smart as everyone.”

Any process, even the bulkiest, most physical process, can be led by bottom-up swarm thinking Take, for example, the delivery of wet cement in the less-than-digital economy of rural northern Mexico Here Cemex (Cementos Mexicanos) runs a ready-mix cement business that

tack-is overwhelming its competitors and attracting worldwide interest It used to be that getting a load of cement delivered on time to a con-struction site in the Guadalajara region was close to a miracle Traffi c delays, poor roads, contractors who weren’t ready when they said they would be, all added up to an on-time delivery rate of less than 35%

In response, cement companies tried to enforce rigid advance tions, which, when things went wrong (as they always did), only made matters worse (“Sorry, we can’t reschedule you until next week.”).Cemex transformed the cement business by promising to deliver concrete faster than pizza Using extensive networking technology—GPS real-time location signals from every truck, massive telecommu-nications throughout the company, and full information available to

reserva-drivers and dispatchers, with the authority to act on it—the company

was able to promise that if your load was more than 10 minutes late, you got a 20% discount

Instead of rigidly trying to schedule everything ahead of time in an environment of chaos, Cemex let the drivers themselves schedule deliv-eries ad hoc and in real time The drivers formed a fl ock of trucks criss-crossing the town If a contractor called in an order for 12 yards of mix, the available truck closest to the site at that time would make the de-livery Dispatchers would ensure customer creditworthiness and guard against omissions, but the agents in the fi eld had permission and the information they needed to schedule orders on the fl y Result: On-time delivery rates reached about 98%, with less wastage of hardened ce-ment, and much happier customers

Similar thinking has been used in a GM paint plant in Fort Wayne, Indiana The wonderful choice of colors that customers now enjoy on new vehicles was playing havoc on the paint line When one car after another is sprayed black, everything is easy But when one car is red and the next white, the painting process is slowed down as painting equip-ment is cleansed of one color to make it ready for the next (The clean-out procedure also wastes paint left in the paint lines.) Why not gang

up all the white cars and do them together? Because ganging up slows the line A car has to be built and completed as it is ordered, as quickly

as possible The solution embraces the swarm

In the paint factory each robot painter (basically a dimwitted ing arm) is empowered to bid on a paint job If it is currently painting red and a car slated to be red is coming down the assembly line, it says,

paint-“Let me do it,” and it beckons the car to its paint station The robots schedule their own work They have very tiny brainlets, connected to

a server No central brain coordinates; the schedule comes from the swarm of mini-brains The result: GM saves $1.5 million a year The

equipment requires less paint (due to less cleaning between cars), and keeps the line moving faster.

Railways are now employing swarm technology Centralized traffi c control doesn’t work when the traffi c becomes very complex and time cycles are shortened The Japanese use a bottom-up swarm model to schedule their famous bullet express trains, which boast incredible punctuality Switching is done locally and autonomously as if the trains were a swarm with one mind Railway owners in Houston are hoping to get a swarm model running for their rail yards With their current cen-trally controlled system, the switching yards are so clogged that there

is a permanent train of freight cars circling the greater Houston area as

a buffer It’s like a mobile parking lot When there’s an opening in the yard, cars are pulled out of the holding pattern train But with a system based on the swarm model, local lines can autonomously switch them-selves, using minimal intelligence onboard Such a self-regulating and self-optimizing system would reduce delays

That’s how the internet handles its amazing loads of traffi c Every email message is broken into bits, with each bit addressed in an en-velope, and then all the fragmentary envelopes are sent into a global web of pathways Each envelope seeks the quickest route it can fi nd instant by instant The email message becomes a swarm of bits that are reassembled at the other end into a unifi ed message If the message is re-sent to the same destination, the second time it may go by a wholly different route Often the paths are ineffi cient Your email may go to Timbuktu and back on its way across town A centralized switching sys-tem would never direct messages in such a wasteful manner But the ineffi ciencies of individual parts is overcome by the incredible reliability

of the system as a whole

The internet model has many lessons for the new economy but haps the most important is its embrace of dumb swarm power The aim

per-of swarm power is superior performance in a turbulent environment When things happen fast and furious, they tend to route around cen -tral control By interlinking many simple parts into a loose confed-eration, control devolves from the center to the lowest or outermost points, which collectively keep things on course

A successful system, though, requires more than simply ing control completely to the networked mob

relinquish-Complete surrender to the bottom is not what embracing swarm is about.

Let me retell a story that I told in Out of Control, a book that details

the advantages, disadvantages, quirks, and consequences of complex systems governed by swarmlike processes This story illustrates the power of a swarm, but it has a new ending, which shows how dumb power is not always enough

In 1990 about 5,000 attendees at a computer graphics conference were asked to operate a computer fl ight simulator devised by Loren Carpenter Each participant was connected into a network via a virtual joy stick Each of the 5,000 copilots could move the plane’s up/down, left/right controls as they saw fi t, but the equipment was rigged so that the jet responded to the average decisions of the swarm of 5,000 participants The fl ight took place in a large auditorium, so there was lateral communication (shouting) among the 5,000 copilots as they attempted to steer the plane Remarkably, 5,000 novices were able to land a jet with almost no direction or coordination from above One came away, as I did, convinced of the remarkable power of distributed, decentralized, autono mous, dumb control

About fi ve years after the fi rst show (this is the update), Carpenter returned to the same conference with an improved set of simulations, better audience input controls, and greater expectations This time, in-stead of fl ying a jet, the challenge was to steer a submarine through a 3D under sea world to capture some sea monster eggs The same audi-ence now had more choices, more dimensions, and more controls The sub could go up/down, forward/back, open claws, close claws, and so

on, with far more liberty than the jet had When the audience fi rst took command of the submarine, nothing happened Audience members wiggled this control and that, shouted and counter-shouted instruc-tions to one another, but nothing moved Each person’s instructions were being canceled by another person’s orders There was no cohesion The sub didn’t budge

Finally Loren Carpenter’s voice boomed from a loudspeaker in the back of the room “Why don’t you guys go to the right?” he hollered Click! Instantly the sub zipped of to the right With emergent coordina-tion the audience adjusted the details of sailing and smoothly set off in

search of sea monster eggs.

Loren Carpenter’s voice was the voice of leadership His short sage carried only a few bits of information, but that tiniest speck of top-down control was enough to unleash the swarm below He didn’t steer the sub The audience of 5,000 novice cocaptains did that very compli-cated maneuvering, magically and mysteriously All Loren did was un-lock the swarm’s paralysis with a vision of where to aim The swarm again fi gured out how to get there in the same marvelous way that they had fi gured out how to land the jet fi ve years earlier

mes-Without some element of governance from the top, bottom-up trol will freeze when options are many Without some element of leader- ship, the many at the bottom will be paralyzed with choices.

con-Numerous small things connected together into a network generate tremendous power But this swarm power will need some kind of mini-mal governance from the top to maximize its usefulness Appropriate oversight depends on the network In a fi rm, leadership is supervision;

in social networks, government; in technical networks, standards and codes

We have spent centuries obsessed with the role of top-down ernance Its importance remains But the great excitement of the new economy is that we have only now begun to explore the power of the bottom, where peers holds sway It is a vast mother lode waiting to be tapped With the invention of a few distributed systems, such as the in-ternet, we have merely probed the potential of what minimally central-ized networks can do

gov-At present, there is far more to be gained by pushing the ies of what can be done by the bottom than by focusing on what can be done at the top.

boundar-When it comes to control, there is plenty of room at the bottom What we are discovering is that peer-based networks with millions of parts, minimal oversight, and maximum connection among them can

do far more than anyone ever expected We don’t yet know what the limits of decentralization are

The great benefi ts reaped by the new economy in the coming ades will be due in large part to exploring and exploiting the power of decentralized and autonomous networks.

dec-First we make a chip for every object Then we connect them We continue to connect all humans We enlarge our conversation to in-clude the world, and all its artifacts We let the network of objects gov-ern itself as much as possible; we add government where needed In this matrix of connections, we interact and create This is the net that is our future

The whole process won’t be completed by tomorrow, but the tiny is clear We are connecting all to all, until we encompass the entire human-made world And in that embrace is a new power

des-Strategies

Move technology to invisibility As technology becomes ubiquitous

it also becomes invisible The more chips proliferate, the less we will notice them The more networking succeeds, the less we’ll be aware of it

In the early 1900s, at the heroic stage of the industrial economy, motors were changing the world Big, heavy motors ran factories and trains and the gears of automation If big motors changed work, they were sure to change the home, too So the 1918 edition of the Sears, Roebuck catalog featured the Home Motor—a fi ve-pound electrical beast that would “lighten the burden of the home.” This single Home Motor would supply all the power needs of a modern family Also for sale were plug-ins that attached to the central Home Motor: an egg beater device, a fan, a mixer, a grinder, a buffer Any job that needed do-ing, the handy Home Motor could do Marc Weiser, a scientist at Xerox, points out that the electric motor succeeded so well that it became in-visible Eighty years later nobody owns a Home Motor We have instead dozens of micro motors everywhere They are so small, so embedded, and so common that we are unconscious of their presence We would have a hard time just listing all the motors whirring in our homes today (fans, clocks, water pumps, video players, watches, etc.) We know the

industrial revolution succeeded because we can no longer see its diers, the motors.

sol-Computer technology is undergoing the same disappearance If the information revolution succeeds, the standalone desktop computer will eventually vanish Its chips, its lines of connection, even its visual interfaces will submerge into our environment until we are no longer conscious of their presence (except when they fail) As the network age matures, we’ll know that chips and glass fi bers have succeeded only when we forget them Since the measure of a technology’s success

is how invisible it becomes, the best long-term strategy is to develop products and services that can be ignored

If it is not animated, animate it Just as the technology of writing

now covers almost everything we make (not just paper), so too the technologies of interaction will soon cover all that we make (not just computers) No artifact will escape the jelly bean chip; everything can

be animated Yet even before chips reach the penny price, objects can

be integrated into a system as if they are animated Imagine you had a

million disposable chips What would you do with them? It’s a good bet that half of the value of those chips could be captured now, with exist-ing technology, by creating a distributed swarmlike intelligence using such dumb power

If it is not connected, connect it As a fi rst step, every employee of an

institution should have intimate, easy, continuous access to the tion’s medium of choice—email, voicemail, radio, whatever The ben-efi ts of communication often don’t kick in until ubiquity is approached; aim for ubiquity Every step that promotes cheap, rampant, and univer-sal connection is a step in the right direction

institu-Distribute knowledge Use the minimal amount of data to keep all

parts of a system aware of one another If you operate a parts house, for example, your system needs to be knowledgeable of each part’s location every minute That’s done by barcoding everything But

ware-it needs to go further Those parts need to be aware of what the tem knows The location of parts in a warehouse should shift depend-ing on how well they sell, what kind of backlog a vendor forecasts, how their substitutes are selling The fastest-moving items (which will be

sys-a dynsys-amic list) msys-ay wsys-ant to be positioned for esys-asier picking sys-and

ship-ping The items move in response to the outside—if there is a system

to spread the info

Get machines to talk to one another directly Information should

fl ow laterally and not just into a center, but out and between as well The question to ask is, “How much do our products/services know about our business?” How much current knowledge fl ows back into the edges? How well do we inform the perimeter, because the perimeter is the center of action

If you are not in real time, you’re dead Swarms need real-time

com-munication Living systems don’t have the luxury of waiting overnight

to process an incoming signal If they had to sleep on it, they could die

in their sleep With few exceptions, nature reacts in real time With few exceptions, business must increasingly react in real time High transac-tion costs once prohibited the instantaneous completion of thousands

of tiny transactions; they were piled up instead and processed in effective batches But no longer Why should a phone company get paid only once a month when you use the phone every day? Instead it will eventually bill for every call as the call happens, in real time The fl ow

cost-of crackers cost-off grocery shelves will be known by the cracker factory in real time The weather in California will be instantly felt in the assembly lines of Ohio Of course, not all information should fl ow everywhere; only the meaningful should be transmitted But in the network economy only signals in real time (or close to it) are truly meaningful Examine the speed of knowledge in your system How can it be brought closer

to real time? If this requires the cooperation of subcontractors, distant partners, and far-fl ung customers, so much the better

Count on more being different A handful of sand grains will never

form an avalanche no matter how hard one tries to do it Indeed one could study a single grain of sand for a hundred years and never con-clude that sand can avalanche To form avalanches you need millions

of grains In systems, more is different A network with a million nodes acts signifi cantly different from one with hundreds The two networks are like separate species—a whale and an ant, or perhaps more accu-rately, a hive and an ant Twenty million steel hammers swinging in uni-son is still 20 million steel hammers But 20 million computers in a swarm is much, much more than 20 million individual computers

Do what you can to make “more.” In a network the chicken-and-egg problem can hinder growth at fi rst—there’s no audience because there

is no content, and there is no content because there is no audience Thus, the fi rst efforts at connecting everything to everything sometimes yield thin fruit At fi rst, smart cards look no different from credit cards—just more inconvenient But more is different; 20 million smart cards is

a vastly different beast than 20 million credit cards

It’s the small things that change the most in value as they become

“more.” A tiny capsule that beeps and displays a number, multiplied by millions: the pager system What if all the Gameboys or Playstations in the world could talk to one another? What if all the residential electric meters in a city were connected together into a large swarm? If all the outdoor thermometers were connected, we would have a picture of our climate a thousand times better than we have ever had before

The ants have shown us that there is almost nothing so small in the world that it can’t be made larger by embedding a bit of interaction in many copies of it, and then connecting them all together

The game in the network economy will be to fi nd the overlooked small and fi gure out the best way to have them embrace the swarm

This amazing boom is not hard to visualize Take 4 acquaintances; there are 12 distinct one-to-one friendships among them If we add a

fi fth friend to the group, the friendship network increases to 20 different relations; 6 friends makes 30 connections; 7 makes 42 As the number

of members goes beyond 10, the total number of relationships among

the friends escalates rapidly When the number of people (n) involved is

large, the total number of connections can be approximated as simply

n × n, or n2 Thus a thousand members can have a million friendships.

The magic of n2 is that when you annex one more new member, you

*I use the vernacular meaning of “exponential” to mean “explosive

com-pounded growth.” Technically, n2 growth should be called polynomial, or even

more precisely, a quadractic; a fi xed exponent (2 in this case) is applied to a

grow-ing number n True exponential growth in mathematics entails a fi xed number (say 2) that has a growing exponent, n, as in 2n The curves of some polynomials

and exponentials look similar, except the exponential is even steeper; in common discourse the two are lumped together.

add many more connections; you get more value than you add That’s not true in the industrial world Say you owned a milk factory, and you had 10 customers who bought milk once a day If you increased your customer base by 10% by adding one new customer, you could expect

an increase in milk sales of 10% That’s linear But say, instead, you owned a telephone network with 10 customers who talked to each other

once a day Your customers would make about n2 (102), or 100 calls a

day If you added one more new customer, you increased your customer base by 10%, but you increased your calling revenue by a whopping 20% (since 112 is 20% larger than 102) In a network economy, small efforts can lead to large results

A network’s tendency to explode in value mathematically was fi rst noticed by Bob Metcalfe, the inventor of a localized networking technol-ogy called Ethernet During the late 1970s Metcalfe was selling a combi-nation of Ethernet, Unix, and TCP/IP (the internet protocol), as a way to make large networks out of many small ones Metcalfe says, “The idea

that the value of a network equals n squared came to me after I failed

to get networks to work on a small scale, despite many repeated ments.” He noticed that networks needed to achieve critical mass to make them worthwhile But he also noticed that as he linked together small local networks here and there, the value of the combined large network would multiply abruptly In 1980 he began formulating his law:

experi-value = n × n.

In fact, n2 underestimates the total value of network growth As

economic journalist John Browning notes, the power of a network tiplies even faster than this Metcalfe’s observation was based on the idea of a phone network Each telephone call had one person at each end; therefore the total number of potential calls was the grand sum of all possible pairings of people with phones But online networks, like personal networks in real life, provide opportunities for complicated three-way, four-way, or many-way connections You can not only interact with your friend Charlie, but with Alice and Bob and Charlie at the same time The experience of communicating simultaneously with Charlie’s group in an online world is a distinct experience, separate in its essen-tial qualities, from communicating with Charlie alone Therefore, when

mul-we tally up the number of possible connections in a network mul-we have to add up not only all the combinations in which members can be paired,

but also all the possible groups as well These additional combos send the total value of the network skyrocketing The precise arithmetic is not important It is enough to know that the worth of a network races ahead of its input.

This tendency of networks to drastically amplify small inputs leads

to the second key axiom of network logic: the law of increasing returns

In one way or another this law undergirds much of the strange behavior

in the network economy The simplest version goes like this: The value

of a network explodes as its membership increases, and then the value explosion sucks in yet more members, compounding the result

An old saying puts it succinctly: Them that’s got shall get.

A new way of saying it: Networks encourage the successful to be yet more successful Economist Brian Arthur calls this effect “increasing returns.” “Increasing returns” he says, “are the tendency for that which

is ahead to get further ahead; for that which loses advantage to lose further advantage.”

In the industrial economy success was self-limiting; it obeyed the law of decreasing returns In the network economy, success is self- reinforcing; it obeys the law of increasing returns.

We see the law of increasing returns operating in the way areas such

as Silicon Valley grow; each successful new up attracts other ups, which in turn attract more capital and skills and yet more start-ups

self-www.allitebooks.com

(Silicon Valley and other high-tech industrial regions are themselves tightly coupled networks of talent, resources, and opportunities.)

At fi rst glance the law of increasing returns may seem identical to the familiar textbook notion of economies of scale: The more of a prod-uct you make, the more effi cient the process becomes Henry Ford lev-eraged his success in selling automobiles to devise more productive methods of manufacturing cars This enabled Ford to sell his cars more cheaply, which created larger sales, which fueled more innovation and even better production methods, sending his company to the top.That self-feeding circle is a positive feedback loop While the law

of increasing returns and the economies of scale both rely on positive feedback loops, there are two key differences

First, industrial economies of scale increase value gradually and early Small efforts yield small results; large efforts give large results Networks, on the other hand, increase value exponentially—small ef-forts reinforce one another so that results can quickly snowball into an ava lanche It’s the difference between a piggy bank and compounded interest

lin-Second, and more important, industrial economies of scale stem from the herculean efforts of a single organization to outpace the com-petition by creating value for less The expertise (and advantage) de-veloped by the leading company is its alone By contrast, networked increasing returns are created and shared by the entire network Many agents, users, and competitors together create the network’s value Al-though the gains of increasing returns may be reaped unequally by one organization, the value of the gains resides in the greater web of rela-tionships

These positive feedback loops are created by “network externalities.” Anything that creates (or destroys) value which cannot be appointed to someone’s account ledgers is an externality The total value of a tele-phone system lies outside the total internal value of the telephone com-panies and their assets It lies externally in the greater phone network itself Networks are particularly potent sources of external value and have become a hot spot of economic investigation in the last decade A parade of recently published academic papers scrutinize the fi ne points

of network externalities: When do they arise? How do they break down?

Are they symmetrical? Can they be manipulated?

One reason increasing returns and network externalities are ing attention is because they tend to create apparent monopolies Huge amounts of cash pour toward network winners such as Cisco or Oracle

garner-or Microsoft, and that makes everyone else nervous Are netwgarner-ork winners in fact monopolies? They are not like any monopolies of the industrial age When antitrust hearings are conducted today, the wit-nesses are not customers angered by high pricing, haughty service, or lack of options—the traditional sins of a monopolist Customers have nothing to complain about because they get lower prices, better service, and more features from network superwinners—at least in the short term The only ones complaining about superwinners are their com-petitors, because increasing returns create a winner-take-most environ-ment But in the long term, the customer will have reason to complain

super-if competitors pull back or disappear

The new monopolies are different in several ways Traditional nopolies dominated commodities In the new order, as Santa Fe Insti-tute economist Brian Arthur points out, “Dominance may consist not

mo-so much in cornering a single product as in successively taking over more and more threads of the web of technology.” Superwinners can practice a type of crossover where control of one layer of the web lever-ages control into others Owning the standard for voice phone calls can ease the likelihood of owning the standard for fax transmissions

The unacceptable transgression of the traditional monopolist was that as a mono-seller (thus the Greek, mono-polist), it could push prices up and quality down But the logic of the net inherently lowers prices and raises quality, even those of a single-seller monopolist In the network economy, the unpardonable transgression is to stifl e inno-vation, which is what happens when competition is stifl ed In the new order, innovation is more important than price because price is a de-rivative of innovation

Mono-sellers are actually desirable in a network economy Because

of increasing returns and n2 value, a single large pool is superior to

many smaller pools The network economy will breed mono-sellers with great fertility What is intolerable in a network economy is “monova-tion”— depending upon a single source of innovation The danger of

monopolists in the network economy is not that they can raise prices but they can become monovationists But there are ways to encourage

“polyvation”—multiple sources of innovation—in a world of lists: by creating open systems, by moving key intellectual properties into the public domain, by releasing source code democratically As we come to understand the importance of increasing returns and the other new rules of the network economy, we can expect shifts in our under-standing of the role of market winners

monopo-Industrial monopolies exploited simple economies of scale for their own benefi t Network effects are not about economies of scale, they are about value that is created above and beyond a single organization—by

a larger network—and then returned to the parts, often unevenly cause some portion of the value of a network fi rm so obviously comes from external sources, allegiance is often granted to external sources

Be-We see this in the way network effects govern the growth of Silicon Valley Silicon Valley’s success is external to any particular company’s success, and so loyalty is external, too As AnnaLee Saxenian, author of

Regional Advantage, notes, Silicon Valley has in effect become one large,

distributed company People job-hop so frequently that folks “joke that you can change jobs without changing car pools Some say they wake

up thinking they work for Silicon Valley Their loyalty is more to ing technology or to the region than it is to any individual fi rm.”

advanc-This trend seems likely to extend further We are headed into an era when both workers and consumers will feel more loyalty to a network than to any ordinary fi rm The great innovation of Silicon Valley is not the wowie-zowie hardware and software it has invented Silicon Valley’s greatest “product” is the social organization of its companies and, most important, the networked architecture of the region itself—the tangled web of former jobs, intimate colleagues, information leakage from one

fi rm to the next, rapid company life cycles, and agile email culture This social web, suffused into the warm hardware of jelly bean chips and copper neurons, creates a true network economy

The social web, even in the Valley, displays some stress marks There

is no question that the network economy is, at worst, winner-take-all, and at best, winner-take-most The trajectory of increasing returns and

a shortage of attention focuses success toward a few points Stars and hits rise, while the rest languish Mundane appliances and bulky objects

now seem to follow the Hollywood model: A few brands sell like crazy, and the rest sell only a few It’s a “hits” economy, where resources fl ow

to those that show some life If a new novel, new product, or new vice begins to succeed it is fed more; if it falters, it’s left to wither Them that has, gets more

ser-The current great debate is whether the law of increasing returns favors the early or not In some of the fi rst studies of increasing returns, economist Brian Arthur discovered that when technological competi-tors, such as the VHS and Betamax video formats, were modeled in a computer, increasing returns favored one technology over the other—

to the eventual demise of the unfortunate one (in this case Betamax) And “unfortunate” is the right word According to Arthur’s research, the technology that came to dominate, thanks to increasing returns, was not necessarily the superior one It was just the lucky one Or the early one Arthur writes: “If a product or a company or a technology—one of many competing in a market—gets ahead by chance or clever strategy, increasing returns can magnify this advantage, and the product or com-pany can go on to lock in the market.”

All things being equal, early success has a measurable advantage But in real life all things are rarely equal Technologies which seem to

be inferior and yet prevail through the dynamics of increased returns often reveal themselves under further study to be slightly superior in key ways The Sony Betamax format lost to VHS because it couldn’t record for as long as VHS could, and, according to some, because Sony discouraged Beta use for porno—an early use of video Apple Comput-er’s superior operating system lost to Windows because Apple had an inferior price—due to its misguided monopolist strategy The suppos-edly ergonomic Dvorak keyboard lost to the all-too-familiar QWERTY keyboard because the Dvorak layout really wasn’t any faster

Being fi rst or best sometimes helps, but not always The outcome

of competition in a network is not determined solely by the abilities of the competitors, but by tiny differences, including luck, that are greatly magnifi ed by the power of positive feedback loops The fate of com-petition is “path dependent” on minor nudges and hurdles that can

“tip” the system in one direction or another Final destiny cannot be predicted on the basis of exceptional attributes alone

What can be predicted is the way in which networks enlarge small

advantages, and then lock the advantage in In the same way, initial rameters and conventions can quickly freeze into unalterable stan-dards The solidifying standards of a network are both a blessing and a curse—a blessing because the ad hoc agreement reduces risk, and thus sparks widespread progress, and a curse because those who own or control the standard are disproportionately rewarded.

pa-But the network economy doesn’t allow the blessing without the curse Microsoft’s billions are tolerated (more or less) because so many others in the network economy have made their collective billions on the advantages of Microsoft’s increasing-returns standards

We forget how recent and sudden Microsoft’s prominence is soft is a textbook example of Metcalfe’s law (“The value of Windows in-creases exponentially as its users increase arithmetically”) and the law

Micro-of increasing returns (“The more who use NT, the more attractive NT becomes”) Microsoft also illustrates the third corollary of increasing returns: how small signals can suddenly become booms

During its fi rst 10 years, Microsoft’s profi ts were negligible Its its rose above the background noise of Wall Street only around 1985 But once they began to rise, they exploded A chart of Microsoft’s cor-nucopia of profi ts is an exponentially booming curve, one that parallels several other rising stars in the network economy

prof-Federal Express experienced a similar trajectory: years of minuscule profi t increases, slowly ramping up to an invisible threshold, and then surging skyward in a blast sometime during the early 1980s

The story of fax machines is likewise a tale of a 20-year-long night success After two decades of marginal success, the number of fax machines quietly crossed the point of no return during the mid-

Internet Microsoft

Fax FedEx

10 Billion

5 Billion

Network organizations experience small gains while their network is being seeded Once the network is established, explosive growth follows with relatively little additional genius.

1980s—and the next thing you know, they were everywhere.

The archetypal case of a success explosion in a network economy

is the Internet itself As any proud old-time nethead will be happy to explain, the internet was a lonely (but thrilling!) cultural backwater for two decades before it showed up on the media radar A graph of the number of internet hosts worldwide, starting in the 1970s, stays barely above the bottom line, until around 1991, when the global tally of hosts suddenly mushroomed, exponentially acting upward to take over the world

The curves of Microsoft, the internet, fax machines and FedEx (I

owe Net Gain author John Hagel credit for these four examples) are

templates of exponential growth, compounding in a biological way Such curves are almost the defi nition of a biological system That’s one reason the network economy is often described most accurately in bio-logical terms Indeed, if the web feels like a frontier, it’s because for the

fi rst time in history we are witnessing biological growth in technological systems

A good defi nition of a network is organic behavior in a technological matrix.

The compounded successes of Microsoft, FedEx, fax machines, and the internet all hinge on the prime law of networks: Value explodes ex-ponentially with membership, and this heightened value acts like grav-ity drawing in yet more members The virtuous circle infl ates until all potential members are joined

This explosion, however, did not ignite until approximately the late 1980s Two things happened then—the dual big bangs of almost-free jelly bean chips and collapsing telco charges It became feasible—that

is, dirt cheap—to exchange data almost anywhere, anytime The net, the grand net, began to precipitate out of this supersaturated solution Network power followed

One of the hallmarks of the industrial age was its reasonable tations Success was in proportion to effort Small effort, small gains Large effort, large gains This linear ratio is typical of capital invest-ments and resource allotments According to data from the U.S Statis-tical Abstract, the best-selling products in the 1950s—appliances such

expec-as refrigerators, clocks and wexpec-ashing machines—sold steadily with only

a slight 2% annual increase in the number of units sold per year To imagine the future of an enterprise or innovation one needed only to extrapolate the current trends in a straight line There was a comfort-able assumption—largely true—that the world proceeded linearly En-tirely new phenomenona did not ordinarily appear out of nowhere and change everything within months

With the advent of large-scale electronic media networks in the mid century, that assumption began to erode Millions of kids watching

TV grew up to create rapid fads (hula hoops), instant youth cultures such as the beats and hippies, with sudden spontaneous gatherings

of half a million, as at Woodstock Events did not happen linearly With media networks it was no longer safe to extrapolate the future from the recent past When success came, it often fed on itself in crazy hy-perkinetic booms The recent sales of electronic pets is one example Tamagotchis, the original brand of Japanese toy pets, went from sales

of zero in Japan to 10 million units in their fi rst year, to 20 million by the second year When they were introduced in the United States a half million units were sold in the fi rst month The Tamagotchis could be actual breeding animals judging simply from their growth rate because their sales curve follows the population curve of reproducing biological animals One day there are two pets, the next year there are 200 In bio-logical populations, success can easily compound into runaway growth; now this wild runaway growth is happening with technology

Everyday we see evidence of biological growth in technological tems This is one of the marks of the network economy: that biology has taken root in technology And this is one of the reasons why net- works change everything.

sys-Here’s how this happened Most of the technology in the early part

of the century was relegated to the inside of a factory Only men cared about advancing technology—cheaper production methods

business-or mbusiness-ore specialized materials The consumer products this advanced technology spun off into homes were, more often than not, labor-sav-ing devices—sewing machines, vacuum cleaners, water pumps They saved time, and thereby enhanced the prevailing culture But the de-

vices themselves (except for the automobile) were merely gadgets

They were technology—something foreign, best used in small doses,

and clearly not the social and economic center of our lives It was once very easy to ignore technology because it did not penetrate the areas of our lives we have always really cared about: our networks of friendship, writing, painting, cultural arts, relationships, self-identity, civil organi-zations, the nature of work, the acquisition of wealth, and power But with the steady advent of technology into the networks of communica-tion and transportation, technology has completely overwhelmed these social areas Our social space has been invaded by the telegraph, the phonograph, the telephone, the photograph, the television, the airplane and car, then by the computer, and the internet, and now by the web

Technology has become our culture, our culture technology.

Technology is no longer outside, no longer alien, no longer at the periphery It is at the center of our lives “Technology is the campfi re around which we gather,” says musician/artist Laurie Anderson For many decades high tech was marginal in presence Then suddenly—blink—it is everywhere and all-important

Technology has been able to infi ltrate into our lives to the degree it has because it has become more like us It’s become organic in struc-ture Because network technology behaves more like an organism than like a machine, biological metaphors are far more useful than mechani-cal ones in understanding how the network economy runs

But if success follows a biological model, so does failure A ary tale: One day, along the beach, tiny red algae suddenly blooms into

caution-a vcaution-ast red tide A few weeks lcaution-ater, just when the red mcaution-at seems indelible,

it vanishes Lemmings boom, then disappear as suddenly The same ological forces that multiply populations can decimate them The same forces that feed on one another to amplify network presences creating powerful standards overnight can also work in reverse to unravel them

bi-in a blbi-ink The same forces that converge to build up organizations bi-in

so biological a fashion can also converge to tear them down One can expect that when Microsoft’s fortunes falter, their profi ts will plunge in

a curve inversely symmetrical to their success All the self-reinforcing reasons to join a network’s success run in reverse when the success

turns to failure and everyone wants to fl ee.

One more biological insight can be gleaned from the success of Micro soft, FedEx, and the internet In retrospect one can see that at some point in their history the momentum toward them became so overwhelming that success became a runaway event Success became infectious, so to speak, and spread pervasively to the extent that it be-came diffi cult for the uninfected to avoid succumbing Take the arrival

of the phone network How long can you hold out not having a phone? Only 6% of U.S homes are still holding out

In epidemiology, the point at which a disease has infected enough hosts that it must be considered a raging epidemic can be thought of as the tipping point The contagion’s momentum has tipped from push-ing uphill against all odds to rolling downhill with all odds behind it In

bi ology, the tipping points of fatal diseases are fairly high, but in nology, they seem to be triggered at much lower points

tech-There has always been a tipping point in any business, industrial or network, after which success feeds upon itself However, the low fi xed costs, insignifi cant marginal costs, and rapid distribution that we fi nd

in the network economy depresses tipping points below the levels of dustrial times; it is as if the new bugs are more contagious—and more potent It takes a smaller initial pool to lead to runaway dominance, sooner

in-Lower tipping points also mean that the threshold of signifi cance—the period before the tipping point during which a movement, growth,

or innovation must be taken seriously—is also dramatically lower than

it was during the industrial age Detecting developments while they are

Significance

Tipping Point

During the exponential gains peculiar to networks, compounding effects can pass

a point of runaway growth But

it is before this point, before momentum builds, that one needs to pay attention.

beneath this threshold of signifi cance is essential.

Major U.S retailers refused to pay attention to TV home-shopping networks during the 1980s because the number of people watching and buying from them was initially so small and marginalized that it did not meet the established level of retail signifi cance The largest U.S re-tailers work in the realm of hundreds of millions The fi rst TV home shopping was dealing in the realm of thousands Retailers discovered that shoppers would watch 50 hours of home-shopping programs be-fore making their fi rst purchase The retailers considered this horrible news But it turns out “watching others do it” was an initiation ritual Shoppers trust other shoppers Once shoppers were “invested” in the process by watching many others do it successfully, they kept coming back So small numbers grew steadily and then rapidly as more shop-pers brought in yet more shoppers Instead of heeding the new subtle threshold of network economics, the retailers waited until the alarm of the tipping point sounded, which meant, by defi nition, that it was too late for them to cash in

In the past, an innovation’s momentum indicated signifi cance Now,

in the network environment, where biological behavior reigns, signifi cance precedes momentum.

-One fi nal parable rooted in biology In a pond one summer a fl ing lily leaf doubles in size every day until it covers the entire surface of water The day before it completely covers the pond, the water is only half covered, and the day before that, only a quarter covered, and the day before that, only a measly eighth While the lily grows imperceptibly all summer long, only in the last week of the cycle would most bystand-ers notice its “sudden” appearance By then, it is far past the tipping point

oat-The network economy is like a lily pond Most of the pond looks empty, but a few lilies are doubling in size The web, for example, is a leaf doubling every six months Despite the one million web sites to date, the web’s future has just begun Other lily leaves are sprouting along the edges of the pond: MUDs, Irridium phones, wireless data ports, collaborative bots, WebTV, and remote solid state sensors Right now, they are all just itsy-bitsy lily cells brewing at the beginning of a hot